CN103630899B - Method for high-resolution radar compressed sensing imaging of moving object on ground - Google Patents

Abstract

The invention provides a method for high-resolution radar compressed sensing imaging of a moving object on ground. The method obtains relatively accurate imaging results and speed and position information of azimuth of the moving object on the basis of greatly compressing ground motion target echo data, and provides important information for accurate identification of the moving object on ground.

Description

The method of ground moving object high resolution radar compressed sensing imaging

Technical field

The present invention relates to remote sensing and radar imaging technology field, particularly relate to the method for a kind of ground moving object high resolution radar compressed sensing imaging.

Background technology

Synthetic-aperture radar (Synthetic Aperture Radar, SAR) can realize round-the-clock, round-the-clock, high-gain ground static target imaging, realizes distance to high-resolution specifically by bandwidth signals band designs; By the motion of carrier of radar platform, form very long linear array in space equivalently thus realize orientation to high-resolution.But in a lot of Military Application situation, not only there is static target in observation scene, also there are some moving targets.Traditional SAR does not possess detection to moving target and imaging capability, and moving target can only be superimposed upon on static target image with the form defocused.The position of orientation of moving-target in static target SAR image had perpendicular to course speed component will depart from its true bearing position, and the moving-target had along course speed component will occur that in static target SAR image orientation is to blooming effect.

SAR acquisition moving object detection and imaging results is utilized to become current military and civilian area research focus.But improving constantly along with SAR precision, the echo data amount of moving target is sharply increased.The storage capacity of huge data volume to system proposes very high requirement, is also a very large challenge to the transmittability of data channel simultaneously.In the high-quality motive target imaging result of guarantee and under accurately estimating the prerequisite of kinematic parameter, how significantly minimizing echo data amount is of great significance for the development tool of SAR motive target imaging and detection technique.

Summary of the invention

(1) technical matters that will solve

For solving above-mentioned one or more problems, the invention provides the method for a kind of ground moving object high resolution radar compressed sensing imaging, to provide the formation method in a kind of applicable data compression situation.

(2) technical scheme

According to an aspect of the present invention, provide the method for a kind of ground moving object high resolution radar compressed sensing imaging, the method comprises: steps A, and data receiver is to the complex base band echoed signal of terrain object in imaging region with utilize distance to pulse compression and offset the signal processing and obtain and comprise moving target information step B, data receiver is to the described signal comprising moving target information carry out the down-sampled compression process of data Random sparseness, adopt random Gaussian observing matrix as the observing matrix of the down-sampled compression of data, obtain the signal after compression and the signal after this compression is sent to data processing end; Step C, data processing termination receives the signal after described compression according to it at fractional Fourier transform FRFT matrix Ψ _αunder openness and Minimum Entropy criteria, utilize compressed sensing algorithm reconstruct Y _α, obtain the optimal result Y of optimum anglec of rotation α and correspondence _α, determine speed and the positional information of moving target.

(3) beneficial effect

As can be seen from technique scheme, in the method for ground moving object high resolution radar compressed sensing of the present invention imaging, excessive for Ground moving target imaging data volume, be not easy to the problem that data store and transmit, propose obtain down-sampled data along orientation to carrying out Random sparseness sampling and combine the motive target imaging algorithm realization motive target imaging based on compressed sensing.Compared to conventional motion target imaging, the echo data amount of ground moving object significantly can be reduced.

Accompanying drawing explanation

The single-emission and double-receiving radar antenna that Fig. 1 adopts for the embodiment of the present invention and the geometric relationship figure with moving target thereof;

Fig. 2 is LFM signal at the distribution plan of time-frequency domain and fractional order;

Fig. 3 is the process flow diagram of ground moving object high resolution radar compressed sensing formation method;

Fig. 4 is echo data amplitude and the result figure that disappears mutually, wherein:

Fig. 4 A receives by antenna A echo in the map of magnitudes apart from slow time domain;

Fig. 4 B is the map of magnitudes of two passage clutter cancellation back echo signals;

Fig. 4 C is without orientation to velocity compensation process, the result directly utilizing doppler frequency rate corresponding to static target to carry out orientation pulse compression to obtain;

Fig. 4 D is the result directly utilizing Fourier Transform of Fractional Order process;

Fig. 5 be the embodiment of the present invention to motive target imaging result figure, wherein:

Shown in Fig. 5 A to be ratio of compression η be 75% result;

Fig. 5 B is the result obtained the range unit search at moving target P1 place;

Fig. 5 C is the result obtained the range unit search at moving target P2 place;

Fig. 5 D is the final imaging results of two moving targets.

Embodiment

For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in more detail.It should be noted that, in accompanying drawing or instructions describe, similar or identical part all uses identical figure number.The implementation not illustrating in accompanying drawing or describe is form known to a person of ordinary skill in the art in art.

Consider in the echo data after ground static target and clutter cancellation process to only have moving target signal, and observe the moving target quantity in scene be limited, therefore Moving Target Return signal meets openness requirement, thus adopts the echoed signal after compressive sensing theory compressing data to carry out imaging and parameter estimation process.Compressive sensing theory is pointed out, if signal is sparse or sparse at certain transform domain, then high dimensional signal is projected to lower dimensional space with the incoherent observing matrix of transform-based by available one, can with high probability reconstruct original signal from low-dimensional observation by solving-optimizing problem.Can say that CS theory utilizes intelligence sample to replace traditional signal sampling, therefore sampling rate is no longer determined by signal bandwidth, but depends on information structure in the signal and content.Therefore, CS theory is applied to Ground moving target imaging system, effectively can realizes the compression to ground Moving Target Return data and imaging processing.

In recent years, as process non-stationary signal, especially the strong instrument of the one of chirp signal, receives and pays close attention to widely fraction Fourier conversion (fractional Fourier transform is called for short FRFT).FRFT is the popularization of traditional F ourier conversion, and signal x (t) angle is that the FRFT of α is defined as:

$X_{\mathrm{\α}}\left(u\right)=F_p\left[x\left(t\right)\right]={\∫}_{-\∞}^{+\∞}x\left(t\right)K_{\mathrm{\α}}(t,u)\mathrm{dt}$

Wherein, p is the exponent number of FRFT, can be arbitrary real number; α=p pi/2; F _pthe operator notation that [ ] is FRFT, K _αthe kernel function that (t, u) is FRFT,

$K_{\mathrm{\α}}(t,u)=\left\{\begin{array}{cc}\mathrm{\δ}(t-u),& \mathrm{\α}=2\mathrm{n\π}\\ \mathrm{\δ}(t+u),& \mathrm{\α}=(2n\±1)\mathrm{\π}\\ \sqrt{(1-j\cot \mathrm{\α})/2\mathrm{\π}}\·\exp (j(t^2+u^2)\cot \mathrm{\α}/2-\mathrm{jtu}\csc \mathrm{\α}).& \mathrm{otherwise}\end{array}\right.$

A time-limited LFM signal is rendered as the dorsal fin shape distribution of skew lines on time-frequency plane, and FRFT conversion is in essence to signal " rotation ", the suitable anglec of rotation is selected to carry out Fourier conversion to signal, can make signal on a certain specific Fractional Fourier Domain, be rendered as the gathering of energy, there is obvious peak value in its amplitude.

As document " Tao Ran, Zhang Feng, Wang Yue.Progress in the discretizationof fractional Fourier transform.Sci China Inf Sci, 2008, 38 (4): 481-503. " progress of discrete fractional Brownian random field is reviewed, document " Deng Bin, Qin Yuliang, Wang Hongqiang, Deng. a kind of SAR moving object detection based on FRFT of improvement and formation method. electronics and information journal, 2008, 30 (2): 326-330. " Fourier Transform of Fractional Order of improvement is applied to SAR moving object detection and imaging.Because SAR moving target orientation can be approximately chirp signal to echoed signal, its expression on time frequency plane is oblique blade-like, has obvious time-frequency characteristic, therefore utilizes FRFT can realize imaging processing and the action reference variable of moving target.

The invention provides a kind of ground moving object high resolution radar compressed sensing formation method, significantly compress Moving Target Return data basis, ground obtains accurately imaging results and orientation to movement velocity and orientation to the estimation of positional information, for the accurate identification of ground moving object provides information.

Conveniently understand, first observation model is described in detail.SAR antenna adopts single-emission and double-receiving configuration mode.Wherein antenna A transmits, and antenna A and antenna B receives echoed signal simultaneously, and antenna A and antenna B phase center distance are d.As shown in Figure 1, SAR is operated in band pattern, and its antenna is positive side-looking, and carrier of radar flying speed is v _a, with radar along course for X-axis, vertical course is that Y-axis sets up coordinate system.Point O is starting point, and making its X-axis coordinate be 0, is R with the bee-line of Texas tower ₀.Point P _nrepresent the n-th moving target in scene, the X-axis coordinate of range points O is x _n, be R with the bee-line of Texas tower _n, be respectively v perpendicular to course and the speed being parallel to course _nyand v _nx.R _anfor antenna A and P _ndistance, R _bnfor antenna B and P _ndistance.

In one exemplary embodiment of the present invention, provide a kind of ground moving object high resolution radar compressed sensing formation method.As shown in Figure 4, the present embodiment method comprises:

Steps A: data receiver is to the complex base band echoed signal obtaining terrain object in imaging region with distance is utilized to process to pulse compression, offset etc. the signal obtaining and comprise moving target information

Wherein, this step can be divided into following sub-step again:

Sub-step A1: the complex base band echoed signal obtaining terrain object in imaging region with $s_B(\hat{t},t_m);$

Suppose that the baseband form of radar transmitted pulse signal is:

$p(\hat{t},t_m)=\mathrm{rect}\left(\frac{\hat{t}}{T_p}\right)\exp \left[\mathrm{j\π\γ}{\hat{t}}^2\right]---\left(1\right)$

Wherein, rect () representation unit rectangular window function, γ is frequency modulation rate, T _p=1/PRF is fire pulse width, and PRF is pulse repetition rate, for the fast time, t _mfor the slow time, m is slow time series number.Transmit through the reflection of imaging region internal object, the complex base band echoed signal that antenna A receives the complex base band echoed signal received with antenna B can be expressed as respectively:

$s_A(\hat{t},t_m)=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{\mathrm{\σ}}_l\·p(\hat{t}-\frac{{2R}_{\mathrm{Al}}\left(t_m\right)}{c})\·\exp \left\{\frac{-j4\mathrm{\π}{R}_{\mathrm{Al}}\left(t_m\right)}{\mathrm{\λ}}\right\}---(2-1)$

$s_B(\hat{t},t_m)=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{\mathrm{\σ}}_l\·p(\hat{t}-\frac{{2R}_{\mathrm{Bl}}\left(t_m\right)}{c})\·\exp \left\{\frac{-j4\mathrm{\π}{R}_{\mathrm{Bl}}\left(t_m\right)}{\mathrm{\λ}}\right\}---(2-2)$

Wherein, L is the target sum in observation scene; σ _lit is target P _lbackscattering coefficient, λ is the wavelength that transmits.R _al(t _m) be t _mmoment antenna A and point target P _ldistance, R _bl(t _m) be t _mmoment antenna B and point target P _tdistance, can be expressed as:

$R_{\mathrm{Al}}\left(t_m\right)=\sqrt{{(R_{\mathrm{Al}}-v_{\mathrm{ly}}{t}_m)}^2+{(v_a{t}_m-d/2-v_{\mathrm{lx}}{t}_m-x_l)}^2}---(3-1)$

$R_{\mathrm{Bl}}\left(t_m\right)=\sqrt{{(R_{\mathrm{Bl}}-v_{\mathrm{ly}}{t}_m)}^2+{(v_a{t}_m+d/2-v_{\mathrm{lx}}{t}_m-x_l)}^2}---(3-2)$

Wherein, $R_{\mathrm{Al}}=\sqrt{R_l^2+{(d/2-x_l)}^2},$ $R_{\mathrm{Bl}}=\sqrt{R_l^2+{(-d/2-x_l)}^2}.$

Sub-step A2: the complex base band echoed signal that antenna A and antenna B is received with carry out distance respectively to pulse compression, obtain with

Right do about the time fourier transform, carry out conjugate multiplication with the frequency-region signal transmitted, and do about the time inverse Fourier transform, obtain range pulse compression process after signal:

${\tilde{s}}_A(\hat{t},t_m)=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{\mathrm{\σ}}_l\sin c\left[B_r(\hat{t}-{2R}_{\mathrm{Al}}\left(t_m\right)/c)\right]\exp (-j4\mathrm{\π}{R}_{\mathrm{Al}}\left(t_m\right)/\mathrm{\λ})---(4-1)$

Wherein, B _r=| r|T _pfor transmitted signal bandwidth, sinc (x)=sin (π x)/(π x).In like manner, range pulse compression process is carried out to the echoed signal that antenna B receives, can obtain:

${\tilde{s}}_B(\hat{t},t_m)=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{\mathrm{\σ}}_l\sin c\left[B_r(\hat{t}-{2R}_{\mathrm{Bl}}\left(t_m\right)/c)\right]\exp (-j4\mathrm{\π}{R}_{\mathrm{Bl}}\left(t_m\right)/\mathrm{\λ})---(4-2)$

Sub-step A3: the complex base band echoed signal after pulse compression of adjusting the distance with adopt and twin-channelly offset process, obtain binary channels offset process after signal

Known according to formula (3):

R _Al(t _m)≈R _Al-v _lyt _m+[(v _a-v _lx)t _m+d/2-x _l] ²/(2R _Al) (5)

Due to τ=d/v _a, then t _m+ τ moment antenna B moves to t _mthe antenna A place in moment, now antenna B and point target P _ldistance, can be expressed as: $R_{\mathrm{Bl}}(t_m+\mathrm{\τ})=\sqrt{{(R_{\mathrm{Al}}-v_{\mathrm{ly}}(t_m+\mathrm{\τ}))}^2+{(v_a{t}_m+d/2-v_{\mathrm{lx}}(t_m+\mathrm{\τ})-x_l)}^2}$ $\≈R_{\mathrm{Al}}-v_{\mathrm{ly}}(t_m+\mathrm{\τ})+[(v_a-v_{\mathrm{lx}})t_m-v_{\mathrm{lx}}\mathrm{\τ}+d/2-x_l{]}^2/\left({2R}_{\mathrm{Al}}\right)---\left(6\right)$

Offseting process to realize land clutter, using deduct in observation scene after offseting process, the information of static target is cancelled out each other, and the phase information of moving target remains unchanged, assuming that moving target number is wherein N, then the signal obtained is: $s_{\mathrm{mov}}(\hat{t},t_m)=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\σ}}_n\sin c\left[B_r(\hat{t}-{2R}_{\mathrm{An}}\left(t_m\right)/c)\right]\exp (-j4\mathrm{\π}{R}_{\mathrm{An}}\left(t_m\right)/\mathrm{\λ})---\left(7\right)$

Steps A 4: right carry out peak value searching, obtain the signal comprising moving target information ${\tilde{s}}_{\mathrm{mov}}\left(t_m\right).$

Because data compression of the present invention is for Data in Azimuth Direction, for perpendicular to course speed v _lyclassic method can be adopted to carry out estimating and compensation deals.For outstanding description step of the present invention, so place can suppose v _ly=0, and put aside R _an(t _m) in about t _mthe impact of quadratic component, so, formula (7) can be reduced to about slow time t _mchirp signal form, namely $s_{\mathrm{mov}}(\hat{t},t_m)=\underset{n=1}{\overset{N}{\∑}}{\mathrm{\σ}}_n\sin c\left[B_r(\hat{t}-{2R}_{\mathrm{An}}/c)\right]\exp (-j4\mathrm{\π}{R}_{\mathrm{An}}/\mathrm{\λ})\exp (\mathrm{j\π}{k}_a{t}_m^2+2\mathrm{j\πf}{t}_m+j\mathrm{\φ})---\left(8\right)$

Wherein, $\left\{\begin{array}{c}{k}_a=-\frac{{2(v_a-v_{\mathrm{nx}})}^2}{R_{\mathrm{An}}\mathrm{\λ}}\\ f=\frac{2(-d/2+x_n)(v_a-v_{\mathrm{nx}})}{R_{\mathrm{An}}\mathrm{\λ}}\\ \mathrm{\φ}=\frac{-2\mathrm{\π}{(-d/2+x_n)}^2}{R_{\mathrm{An}}\mathrm{\λ}}\end{array}\right.---\left(9\right)$

Like this by right peak value searching, the fast time that moving target signal place range gate is corresponding can be obtained and then obtain the distance of moving target and antenna A, thus obtain its radial distance R _n.Formula (9) can be reduced to further simultaneously ${\tilde{s}}_{\mathrm{mov}}\left(t_m\right)=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\σ}}_n\exp (-j4\mathrm{\π}{R}_{\mathrm{An}}/\mathrm{\λ})\exp (\mathrm{j\π}{k}_a{t}_m^2+2\mathrm{j\πf}{t}_m+\mathrm{j\φ})---\left(10\right)$

As can be seen from formula (10), multiple chirp Signal averaging can be regarded as, because Fourier Transform of Fractional Order (FRFT) has good detection perform to chirp signal, therefore utilize FRFT to carry out motive target imaging process and parameter estimation process below.

Step B: data receiver is to the signal comprising moving target information carry out the down-sampled compression process of data Random sparseness, adopt random Gaussian observing matrix as the observing matrix of the down-sampled compression process of data Random sparseness, obtain the signal after compression and this signal is sent to data processing end;

Adopt random Gaussian observing matrix as the observing matrix of the down-sampled compression of data, be designated as Φ, size is echo data after can compressing like this its size is N ' × 1, have compressed compared to raw radar data ratio of compression such observation process can be expressed as by following expression formula ${{\tilde{s}}^{\′}}_{\mathrm{mov}}\left(t_m\right)=\mathrm{\Φ}\·{\tilde{s}}_{\mathrm{mov}}\left(t_m\right)---\left(11\right)$

Data after compression can reduce the pressure of disk storage device or data transmission link.

Step C: data processing termination receives the signal after compression according to it at fractional Fourier transform FRFT matrix Ψ _αunder openness and Minimum Entropy criteria, utilize compressed sensing algorithm reconstruct Y _α, obtain the optimal result Y of optimum anglec of rotation α and correspondence _α, determine speed and the positional information of moving target.

Wherein, this step C can be divided into following sub-step again:

Step C1: structure fractional Fourier transform FrFT matrix Ψ _α, the value of anglec of rotation α at [-π, π], with 0.01 π for step-length.Make ${{\tilde{s}}^{\′}}_{\mathrm{mov}}\left(t_m\right)=\mathrm{\Φ}\·{\tilde{s}}_{\mathrm{mov}}\left(t_m\right)=\mathrm{\Φ}\·{\mathrm{\Ψ}}_{\mathrm{\α}}^{-1}\·Y_{\mathrm{\α}}---\left(12\right)$

The anglec of rotation is the FRFT matrix Ψ of α _αbe configured with various ways, the make that the feature based proposed in document " Tao Ran; ZhangFeng; Wang Yue.Progress in the discretization of fractional Fourier transform.Sci China Inf Sci; 2008; 38 (4): 481-503. " decomposes, because the doppler cells number of raw radar data is so matrix Ψ _αsize be it is specifically expressed as:

Wherein, be the proper vector of the approximate continuity Hermite-Gauss function utilizing matrix Ξ to obtain, the expression formula of matrix Ξ is: $\mathrm{\Ξ}=\left[\begin{array}{cccccc}2& 1& 0& ...& 0& 1\\ 1& 2\cos \left(w\right)& 1& ...& 0& 0\\ 0& 1& 2\cos \left(2w\right)& ...& 0& 0\\ \·& \·& \·& \·& \·& \·\\ \·& \·& \·& \·& \·& \·\\ \·& \·& \·& \·& \·& \·\\ 0& 0& 0& ...& 2\cos \left[(\hat{N}-2)w\right]& 1\\ 1& 0& 0& ...& 1& 2\cos \left[(\hat{N}-1)w\right]\end{array}\right]---\left(14\right)$

Wherein, $w=2\mathrm{\π}/\hat{N}.$

Step C2: set up compressed sensing reconstruction model: $\min {\left|\right|Y_{\mathrm{\α}}\left|\right|}_{0}s.t.{{\tilde{s}}^{\′}}_{\mathrm{mov}}\left(t_m\right)={{\mathrm{\Φ\Ψ}}_{\mathrm{\α}}}^{-1}{Y}_{\mathrm{\α}},---\left(15\right)$

Level and smooth L0 algorithm is utilized to obtain Y _α.Wherein, level and smooth L0 algorithm is specific as follows:

I () makes $\mathrm{\Ω}=\mathrm{\Φ}\·{\mathrm{\Ψ}}_{\mathrm{\α}}^{-1},$ $\tilde{X}={{\tilde{s}}^{\′}}_{\mathrm{mov}}\left(t_m\right),$ $Y_{\mathrm{\α}}^{\left(0\right)}={\mathrm{\Ω}}^{+}\·\tilde{X},$ Ω ⁺=Ω ^h(Ω Ω ^h) ^-1for the generalized inverse of Ω;

(ii) descending series [θ is selected ₁, θ ₂..., θ _j], general θ ₁be set to 2 to 4 times, θ _jbe 0.001, the ratio η that successively decreases=θ _j/ θ _j+1=0.4, expression rounds up, and gets j=1;

(iii) get k=1;

(iv) compute gradient: wherein, the hadamard product of symbol ο representing matrix; Make Y ' _α=Y ' _α-u δ _k, wherein u is little normal number, and suggestion value is 2;

V () makes wherein, the conjugate transpose of symbol H representing matrix.If k≤K (K is positive integer, and suggestion value is 3), then return step (iv), otherwise continue to perform next step.

(vi) judge, if j < is J, j=j+1, returns step (iii), otherwise stops circulation, exports Y _α=Y ' _α.

Step C3: utilize minimum entropy method to choose the estimated value α of the optimum anglec of rotation _aes, the result that wherein entropy is minimum as final motive target imaging result, and obtains the orientation of ground moving object to speed v by following formulae discovery _nxwith orientation to positional information x _n: ${\mathrm{\&alpha;}}_{\mathrm{aes}}=\underset{\mathrm{\&alpha;}}{\min }\{-\underset{N=1}{\overset{\hat{N}}{\mathrm{\&Sigma;}}}|{Y^{\&prime;}}_{\mathrm{\&alpha;}}\left(n\right){|}^2\ln \left({|Y^{\&prime;}}_{\mathrm{\&alpha;}}\left(n\right){|}^2\right)\}---\left(16\right)$ $\left\{\begin{array}{c}{v}_{\mathrm{nx}}=v_a-\sqrt{-\frac{{\mathrm{PRF}}^2\&CenterDot;{\cot \mathrm{\&alpha;}}_{\mathrm{aes}}\&CenterDot;R_{\mathrm{An}}\&CenterDot;\mathrm{\&lambda;}}{2\hat{N}}}\\ x_n=\frac{\mathrm{PRF}\&CenterDot;u_{\mathrm{aes}}\&CenterDot;{\csc \mathrm{\&alpha;}}_{\mathrm{aes}}\&CenterDot;R_{\mathrm{An}}\&CenterDot;\mathrm{\&lambda;}}{2\hat{N}\&CenterDot;(v_a-v_{\mathrm{nx}})}+\frac{d}{2}\end{array}\right.---\left(17\right)$

Wherein, for aspect is to doppler cells number, with it is the scale factor that dimensional normalization is introduced.

Below provide the simulation experiment result based on above-described embodiment method.

Initial parameter relevant in emulation experiment is as follows: the positive side-looking work of radar, the speed v of carrier aircraft _afor 150m/s, carrier aircraft course line is to the distance R of ground imaging center ₀for 10km, transmit carrier frequency f ₀for 10GHz (wavelength 0.03m), pulse width T _pfor 1.2us, bandwidth B is 150MHz, and the range resolution so obtained is 1m.Pulse repetition rate PRF is 500, and imaging integration time is 1s, and the azimuth resolution of acquisition is 1m.The spacing d of antenna A and B is 3m.Suppose that have 14 static targets, 2 moving targets, reflection coefficient is 1 in observation scene.Static target random arrangement, making the initial position of moving target P1 for (9975m, 30m) movement velocity is (0m/s, 20m/s), the initial position of moving target P2 is (10030m, 0m), movement velocity is (0m/s ,-30m/s).Because static scattering point echo is the land clutter that will suppress, the signal to noise ratio (SCR, signal clutter ratio) of distance each antenna echo before pulse compression is about-6.36dB.

First carry out pulse pressure to the echo data received and land clutter offsets process, Fig. 4 A receives by antenna A echo in the map of magnitudes apart from slow time domain, and Fig. 4 B is the map of magnitudes of two passage clutter cancellation back echo signals.From Fig. 4 A, comprise land clutter and moving target signal in the echoed signal of antenna A simultaneously, cannot differentiate and come.After two passage DPCA offset process, in Fig. 4 B, only comprise moving target signal, therefore can carry out the estimation of subsequent motion target component.Fig. 4 C is without orientation to velocity compensation process, the result directly utilizing doppler frequency rate corresponding to static target to carry out Azimuth Compression to obtain.Fig. 4 D is the result directly utilizing Fourier Transform of Fractional Order process.Contrast this two width figure visible, obtaining result without compensation deals does not upwards have well focussed in orientation, and it is even more serious simultaneously can to find out that moving target P2 defocuses than P1, this be due to the orientation of P2 to speed higher than the orientation of P1 to speed.And obtain good result based on FRFT process, the information of two moving targets can be obtained accurately.

Afterwards, the above-mentioned echo data offseting process is compressed, shown in Fig. 5 A to be ratio of compression η be 75% result, Fig. 5 B is the result obtained the range unit search at moving target P1 place, Fig. 5 C is the result obtained the range unit search at moving target P2 place, Fig. 5 D is the final imaging results of two moving targets, as can be seen from imaging results, utilizes the present invention accurately to achieve the imaging processing of ground moving object when significantly compression and back wave datum.

Finally, the optimum exponent number of the moving target utilizing above-mentioned search to obtain, carries out the parameter estimation of moving target.Utilize formula (15) to obtain by the optimum anglec of rotation and u value: the centre frequency of moving target 1 is 27.43Hz, orientation is-115.59Hz/s to the frequency modulation rate of signal ²; The centre frequency that moving target 2 obtains is 2.63Hz, and orientation is-218.64Hz/s to the frequency modulation rate of signal ².Recycling formula (21) solves, and the orientation of the moving target 1 of acquisition is 18.33m/s to speed, and orientation is 32.75m to initial position.The orientation of moving target 2 is-31.10m/s to speed, and orientation is 2.32m to initial position.Can find out and estimate that the moving target orientation that obtains to speed and position and actual value relatively, can realize the description to moving target motion state.

The above simulating, verifying validity of the present embodiment institute extracting method.

So far, by reference to the accompanying drawings the method for the present embodiment ground moving object high resolution radar compressed sensing imaging has been described in detail.Describe according to above, those skilled in the art should have the present invention and have clearly been familiar with.

In sum, the present invention proposes a kind of ground moving object compressed sensing formation method based on discrete fractional Brownian random field, the accurate imaging to ground moving object is achieved in data significantly compression situation, utilize the optimum exponent number of acquisition to carry out the estimation of moving target parameter simultaneously, thus for terrain object motion state describe and identification provide information.

Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (11)

1. a method for ground moving object high resolution radar compressed sensing imaging, is characterized in that, comprising:

Steps A: data receiver is to the complex base band echoed signal of terrain object in imaging region with utilize distance to pulse compression and offset the signal processing and obtain and comprise moving target information

Step B: data receiver adopts random Gaussian observing matrix as observing matrix, to the described signal comprising moving target information carry out the down-sampled compression process of data Random sparseness, obtain the signal after compression and the signal after this compression is sent to data processing end;

Step C: data processing termination receives the signal after described compression according to it at fractional Fourier transform FRFT matrix Ψ _αunder openness and Minimum Entropy criteria, utilize compressed sensing algorithm reconstruct Y _α, obtain the optimal result Y of optimum anglec of rotation α and correspondence _α, determine speed and the positional information of moving target.

2. method according to claim 1, is characterized in that, described steps A comprises:

Sub-step A1: the complex base band echoed signal obtaining terrain object in imaging region with

Sub-step A2: the complex base band echoed signal that antenna A and antenna B is received with carry out distance respectively to pulse compression, obtain distance to the complex base band echoed signal after pulse compression with wherein:

Sub-step A3: to described distance to the complex base band echoed signal after pulse compression with carry out binary channels and offset process, acquisition binary channels offsets the signal after process

Sub-step A4: the signal after process is offseted to binary channels carry out peak value searching, obtain the signal comprising moving target information

3. method according to claim 2, is characterized in that, described sub-step A2 middle distance is as follows to the signal after pulse compression:

${\tilde{s}}_A(\hat{t},t_m)=\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}{\mathrm{\&sigma;}}_l\&CenterDot;\sin c\left(B_r(\hat{t}-\frac{{2R}_{\mathrm{Al}}\left(t_m\right)}{c})\right)\&CenterDot;\exp \left\{\frac{-j4\mathrm{\&pi;}{R}_{\mathrm{Al}}\left(t_m\right)}{\mathrm{\&lambda;}}\right\}$

${\tilde{s}}_B(\hat{t},t_m)=\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}{\mathrm{\&sigma;}}_l\&CenterDot;\sin c\left(B_r(\hat{t}-\frac{{2R}_{\mathrm{Bl}}\left(t_m\right)}{c})\right)\&CenterDot;\exp \left\{\frac{-j4\mathrm{\&pi;}{R}_{\mathrm{Bl}}\left(t_m\right)}{\mathrm{\&lambda;}}\right\}$

Wherein, for the fast time, t _mfor the slow time, m is slow time series number, σ _lbe l target backscattering coefficient (l=1,2 ..., L), L is the target sum in observation scene, B _rfor transmitted signal bandwidth, R _al(t _m), R _bl(t _m) be respectively t _mthe distance of moment antenna A, antenna B and l target, $\sin c\left(x\right)=\frac{\sin \left(\mathrm{\&pi;x}\right)}{\left(\mathrm{\&pi;x}\right)},$ C is the light velocity, and λ is carrier wavelength, $j=\sqrt{-1}.$

4. method according to claim 3, is characterized in that, carrying out binary channels in described sub-step A3, to offset the signal after process as follows:

$s_{\mathrm{mov}}(\hat{t},t_m)=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&sigma;}}_n\&CenterDot;\sin c\left(B_r(\hat{t}-\frac{{2R}_{\mathrm{An}}\left(t_m\right)}{c})\right)\&CenterDot;\exp \left\{\frac{-j4\mathrm{\&pi;}{R}_{\mathrm{An}}\left(t_m\right)}{\mathrm{\&lambda;}}\right\}$

Wherein, σ _nbe observation scene in the n-th moving target backscattering coefficient (n=1,2 ..., N), N is the moving target sum in observation scene.

5. method according to claim 4, is characterized in that, the signal comprising moving target information in described sub-step A4 is as follows:

${\tilde{s}}_{\mathrm{mov}}\left(t_m\right)=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&sigma;}}_n\exp (-j4\mathrm{\&pi;}{R}_{\mathrm{An}}/\mathrm{\&lambda;})\exp (\mathrm{j\&pi;}{k}_a{t}_m^2+2\mathrm{j\&pi;}{\mathrm{ft}}_m+\mathrm{j\&phi;})$

Wherein, R _anfor the distance of initial time antenna A and the n-th moving target, $k_a=-\frac{2{(v_a-v_{\mathrm{nx}})}^2}{R_{\mathrm{An}}\mathrm{\&lambda;}},f=\frac{2(-d/2+x_n)(v_a-v_{\mathrm{nx}})}{R_{\mathrm{An}}\mathrm{\&lambda;}},\mathrm{\&phi;}=\frac{-2\mathrm{\&pi;}{(-d/2+x_n)}^2}{R_{\mathrm{An}}\mathrm{\&lambda;}},$ V _afor the movement velocity of Texas tower, v _nxbe the n-th moving target orientation to translational speed, d is the distance between antenna A and antenna B, x _nbe the n-th moving target orientation to initial position.

6. method according to claim 5, is characterized in that, the signal in described step B after compression as follows:

${{\tilde{s}}^{\&prime;}}_{\mathrm{mov}}\left(t_m\right)\mathrm{\&Phi;}\&CenterDot;{\tilde{s}}_{\mathrm{mov}}\left(t_m\right)$

Wherein, Φ is gaussian random observing matrix, and size is its each element is the random quantity meeting Gaussian distribution, for column vector length, N ' is length.

7. method according to claim 6, is characterized in that, described step C comprises:

Step C1: structure fractional Fourier transform FRFT matrix Ψ _α, the value of anglec of rotation α, at [-π, π], with 0.01 π for step-length, makes ${{\tilde{s}}^{\&prime;}}_{\mathrm{mov}}\left(t_m\right)=\mathrm{\&Phi;}\&CenterDot;{\tilde{s}}_{\mathrm{mov}}\left(t_m\right)=\mathrm{\&Phi;}\&CenterDot;{\mathrm{\&psi;}}_{\mathrm{\&alpha;}}^{-1}\&CenterDot;Y_{\mathrm{\&alpha;}};$

Step C2: set up compressed sensing reconstruction model: $\min {\left|\right|Y_{\mathrm{\&alpha;}}\left|\right|}_{0}s.t.{{\tilde{s}}^{\&prime;}}_{\mathrm{mov}}\left(t_m\right)=\mathrm{\&Phi;}{{\mathrm{\&Psi;}}_{\mathrm{\&alpha;}}}^{-1}{Y}_{\mathrm{\&alpha;}},$ Level and smooth L0 algorithm is utilized to obtain Y _α;

Step C3: utilize minimum entropy method to choose the estimated value α of the optimum anglec of rotation _aes, the result that wherein entropy is minimum, as final motive target imaging result, calculates the orientation of ground moving object to speed v _nxwith orientation to positional information x _n.

8. method according to claim 7, is characterized in that, the matrix of fractional Fourier transform FRFT described in described step C1 Ψ _αsize be it is specifically expressed as:

Wherein, symbol T representing matrix transposition, be the proper vector of the approximate continuity Hermite-Gauss function utilizing matrix Ξ to obtain, the expression formula of matrix Ξ is:

$\mathrm{\&Xi;}=\left[\begin{array}{cccccc}2& 1& 0& ...& 0& 1\\ 1& 2\cos \left(w\right)& 1& ...& 0& 0\\ 0& 1& 2\cos \left(2w\right)& ...& 0& 0\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ 0& 0& 0& ...& 2\cos \left[(\hat{N}-2w)\right]& 1\\ 1& 0& 0& ...& 1& 2\cos \left[(\hat{N}-1)w\right]\end{array}\right]$

Wherein, $w=2\mathrm{\&pi;}/\hat{N}.$

9. method according to claim 8, is characterized in that, utilizes level and smooth L0 algorithm to obtain Y in described step C2 _αcomprise:

I () makes $\mathrm{\&Omega;}=\mathrm{\&Phi;}\&CenterDot;{\mathrm{\&Psi;}}_{\mathrm{\&alpha;}}^{-1},$ $\tilde{X}={{\tilde{s}}^{\&prime;}}_{\mathrm{mov}}\left(t_m\right),$ $Y_{\mathrm{\&alpha;}}^{\left(0\right)}={\mathrm{\&Omega;}}^{+}\&CenterDot;\tilde{X},{\mathrm{\&Omega;}}^{+}={\mathrm{\&Omega;}}^H{\left({\mathrm{\&Omega;\&Omega;}}^H\right)}^{-1},$ Wherein, the conjugate transpose of symbol H representing matrix;

(ii) descending series [θ is selected ₁, θ ₂..., θ _j], θ ₁be set to 2 to 4 times, θ _jbe 0.001, the ratio η that successively decreases=θ _j/ θ _j+1=0.4, expression rounds up, and gets j=1;

(iii) get ${Y^{\&prime;}}_{\mathrm{\&alpha;}}=Y_{\mathrm{\&alpha;}}^{(j-1)},k=1;$

(iv) compute gradient: wherein, the hadamard product of symbol ο representing matrix, || ²for representing square vector formed of its element amplitude during vector; Make Y' _α=Y' _α-u δ _k, wherein u value is 2;

V () makes if k<K, then return step (iv), otherwise continue to perform next step, wherein, K value is 3;

(vi) judge, if j<J, return step (iii), otherwise stop circulation, export Y _α=Y' _α.

10. method according to claim 9, is characterized in that, is chosen the estimated value α of the optimum anglec of rotation in described step C3 by following formula _aes:

${\mathrm{\&alpha;}}_{\mathrm{aes}}=\underset{\mathrm{\&alpha;}}{\min }\{-\underset{n=1}{\overset{\hat{N}}{\mathrm{\&Sigma;}}}{\left|Y_{\mathrm{\&alpha;}}\left(n\right)\right|}^2\&CenterDot;\ln \left(\right|Y_{\mathrm{\&alpha;}}\left(n\right){|}^2)\}$

Wherein, Y _αn () represents Y _αthe n-th element.

11. methods according to claim 10, is characterized in that, obtain the orientation of ground moving object to speed v in described step C3 by following formulae discovery _nxwith orientation to positional information x _n:

$\left\{\begin{array}{c}{v}_{\mathrm{nx}}=v_a-\sqrt{-\frac{{\mathrm{PRF}}^2\&CenterDot;{\cot \mathrm{\&alpha;}}_{\mathrm{aes}}\&CenterDot;R_{\mathrm{An}}\&CenterDot;\mathrm{\&lambda;}}{2\hat{N}}}\\ x_n=\frac{\mathrm{PRF}\&CenterDot;u_{\mathrm{aes}}\&CenterDot;{\csc \mathrm{\&alpha;}}_{\mathrm{aes}}\&CenterDot;R_{\mathrm{An}}\&CenterDot;\mathrm{\&lambda;}}{2\hat{N}\&CenterDot;(v_a-v_{\mathrm{nx}})}+\frac{d}{2}\end{array}\right.$

Wherein, PRF is pulse repetition rate, u _aesfor the anglec of rotation is α _aestime frequency corresponding to fractional order territory energy peak.